1. Technical field
The present invention relates to an image compression and transmission method, and particularly to a method of relocating a wavelet packet coefficient for a zerotree coding in order to optimize an encoder to be suitable for transmitting image off-line.
2. Description of the Prior Art
This invention is incorporated by the reference, xe2x80x9cImage coding using wavelet transformxe2x80x9d, IEEE Trans. Image Processing, vol.1, pp.205-220, April 1992, by M.Antonini, M.Barlaoud, P.Mathieu, and I.Daubechies; xe2x80x9cEmbedded image coding using zerotree of wavelets coefficientsxe2x80x9d, IEEE Trans. Signal Processing, vol.41, pp.3445-3462, December 1993, by J. M. Sharpiro; xe2x80x9cA new fast and efficient image coding based on set partitioning in hierarchical treesxe2x80x9d, IEEE Trans. Circuits Syst. Video Technol. vol.6, pp.243-250, June 1996, A.Said and W. A. Pearlman; xe2x80x9cBest wavelet packet bases in a rate-distortion sensexe2x80x9d, IEEE Trans. Image Processing, vol.2, pp.160-175, April 1993, by K.Ramchandran and M.Vetterli.
FIG. 1 shows a conventional image compression and transmission method. As shown in the figure, the conventional image compression and transmission method executes a wavelet transform process 10 for dividing image data into several frequency bands, so as to allow most energy to be focused on a few coefficients on the band. Then, the image data is compressed by executing a zerotree coding process 20 of coding the wavelet coefficients by forming a tree structure with the wavelet coefficients.
The image transformed by the wavelet transform process 10 is divided into several frequency bands, as shown in FIG. 2. In the figure, it is indicated that a left and upper portion of the frequency band corresponds to a low frequency band and a right and lower portion also corresponds to a higher frequency band. Therefore, a smallest band, positioned leftmost and uppermost in the frequency band, shows a lowest frequency. Also a largest band, positioned rightmost and lowermost, shows a highest frequency. At this time, because most of the image data energy flock to the low frequency bands, most of the image data can be represented by using only the small frequency bands (low frequency bands). That is, because the energy gathers in a few coefficients, the image data can be compressed by coding only the coefficients.
As shown in FIG. 3, in the zerotree coding process 20, the wavelet transformed coefficients are assembled in a tree structure. In the tree structure, a coefficient in a top position is called xe2x80x9cpatentxe2x80x9d, and coefficients just under the top coefficient, or the parent, are named as xe2x80x9cchildxe2x80x9d. And all coefficients, including the child, under the parent can be commonly designated as xe2x80x9cdescendantxe2x80x9d.
Now, an operation of the above prior art is explained below.
In the wavelet transform process 10, at first, the image is transformed in order to express with a few coefficients in the frequency band. Then in the zerotree coding process 20, the wavelet coefficients are assembled into the tree structure, and a zerotree coding is executed, based on a specific threshold, so as to compress image.
For accomplishing the wavelet transform process 10, a filtering and subsampling is used. The image data can be divided into a low frequency band and a high frequency band by a low frequency filtering and subsampling and a high frequency filtering and subsampling. At this time, because the low frequency band has room to be compressed, the filtering and subsampling is repeated to the low frequency band. As a result, the wavelet transformed image having several frequency bands as shown in FIG. 2 can be obtained.
Then, the zerotree coding is carried out about the wavelet transformed image. In the zerotree coding process, when a magnitude of a coefficient is smaller than a threshold T, the coefficient is defined as xe2x80x9czeroxe2x80x9d or xe2x80x9cinsignificant coefficientxe2x80x9d, while, when a magnitude of a coefficient is bigger than the threshold T, the coefficient is defined as xe2x80x9cnon-zeroxe2x80x9d, or xe2x80x9csignificant coefficientxe2x80x9d. In case of the non-zero, the coefficient value is transmitted after executing quantization about the non-zero coefficient. However, in case of the coefficient defined in zero, the coefficient is defined as a xe2x80x9cZerotree Rootxe2x80x9d symbol when all of the children is zero. In the zero coefficient case, however, when there is non-zero child, the coefficient is defined as a xe2x80x9cIsolated Zeroxe2x80x9d symbol. Then, the wavelet coefficients in the tree structure are scanned and transmitted from parent to child and from left to right in FIG. 3. The wavelet coefficient of image data commonly has strong correlation with a position on a picture. So, when the parent coefficient is zero, the descendant coefficients are much probable to be zero. Therefore, defining that the all zero descendant coefficients as well as the zero parent coefficient are one symbol, that is, a zerotree root, it is permissible not to transmit coefficient values of the descendants in the zerotree root symbol, which gives a high compression effect.
Whole operation of the zerotree coding is accomplished as below. At first, after quantizing all the coefficients on Zerotree on the basis of the initial threshold T, the encoder transmits quantized values of the non-zero coefficients, the isolated zero symbols and the zerotree root symbols. At second, after quantizing all the coefficients on Zerotree on the basis of the second threshold T/2, the encoder transmits secondly calculated quantized values of the non-zero coefficients, the isolated zero symbols and the zerotree root symbols. Then, after repeating the above process successively, when it reaches a required bit rate, the coding is completed.
A decoder performs a reverse operation to the encoder for acquiring a reconstructed image.
It may be understood that the above zerotree coding is designed to be suitable for the wavelet transformed coefficient. As shown in FIG. 2, it is difficult that the tree structure for the zerotree coding is assembled in other form except the wavelet transform.
However, the zerotree coding can not be applied to a wavelet packet transform, which is generalized from the wavelet transform, and provides better performance than the wavelet transform in many kinds of image data. Therefore, there is need for an image compression and transmission technique for applying the zerotree coding to the wavelet packet coefficients.
Therefore, the present invention is designed to overcome the above problems. An object of the invention is to provide a method of relocating wavelet packet coefficients for a zerotree coding. The zerotree coding of the wave packet coefficients can be effectively accomplished by a method which is as follows; collecting coefficients at the same position of different frequency subbands and relocating them at the same position of integrating frequency band.
In order to accomplish the above object, the present invention provides a method for relocating wavelet packet coefficients from frequency subbands into the frequency band integrating the frequency subbands, comprising the steps of collecting coefficients in same locations of the subbands, and relocating the collected coefficients in a corresponding location in an integrating frequency band, formed by integrating the subbands.
Additionally, in another embodiment, the present invention provides a method for relocating wavelet packet coefficients comprising a wavelet packet transform process for executing a filtering and subsampling of image data and transforming the image data into wavelet packet coefficients having various frequency bands; and a coefficient relocating process for collecting coefficients corresponding to same locations on frequency subbands among the wavelet packet transformed coefficients, and relocating the coefficients in a corresponding location on an integrated frequency band so as to assemble a tree structure for a zerotree coding.